Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding
For robotics researchers needing efficient self-collision checking, this is an incremental improvement applying a known technique (positional encoding) to a known problem.
The paper shows that adding positional encoding to the input of an MLP classifier improves self-collision detection accuracy by enabling better capture of high-frequency variations, offering a faster alternative to geometric methods.
This manuscript investigates the integration of positional encoding -- a technique widely used in computer graphics -- into the input vector of a binary classification model for self-collision detection. The results demonstrate the benefits of incorporating positional encoding, which enhances classification accuracy by enabling the model to better capture high-frequency variations, leading to a more detailed and precise representation of complex collision patterns. The manuscript shows that machine learning-based techniques, such as lightweight multilayer perceptrons (MLPs) operating in a low-dimensional feature space, offer a faster alternative for collision checking than traditional methods that rely on geometric approaches, such as triangle-to-triangle intersection tests and Bounding Volume Hierarchies (BVH) for mesh-based models.